Now a new investigation from Human Rights Watch has found that not only are children regularly added to CONARC, but the database also powers a live facial recognition system in Buenos Aires deployed by the city government. This makes the system likely the first known instance of its kind being used to hunt down kids suspected of criminal activity. "It's completely outrageous," says Hye Jung Han, a children's rights advocate at Human Rights Watch, who led the research. Buenos Aires first began trialing live facial recognition on April 24, 2019. Implemented without any public consultation, the system sparked immediate resistance.

The COVID-19 pandemic is the greatest global healthcare crisis of our generation, presenting enormous challenges to medical research, including clinical trials. Advances in machine learning are providing an opportunity to adapt clinical trials and lay the groundwork for smarter, faster and more flexible clinical trials in the future. In an article published in Statistics in Biopharmaceutical Research, an international collaboration of data scientists and pharmaceutical industry experts – led by the Director of the Cambridge Centre for AI in Medicine, Professor Mihaela van der Schaar of the University of Cambridge – describe the impact that COVID-19 is having on clinical trials, and reveal how the latest machine learning (ML) approaches can help to overcome challenges that the pandemic presents. The paper covers three areas of clinical trials in which ML can make contributions: in trials for repurposing drugs to treat COVID-19, trials for new drugs to treat COVID-19, and ongoing clinical trials for drugs unrelated to COVID-19. The team, which includes scientists from pharmaceutical companies such as Novartis, notes that'the pandemic provides an opportunity to apply novel approaches that can be used in this challenging situation.'

In a quiet aisle of a small supermarket in Tokyo, a robot dutifully goes about its work. It looks like a well-integrated autonomous mechanical worker, but that is something of an illusion. This robot doesn't have a mind of its own. Several miles away, a human worker is controlling its every movement remotely and watching via a virtual reality (VR) headset that provides a robot's eye view. This is the work of Japanese firm Telexistence, whose Model-T robot is designed to allow people to do physical labour in supermarkets and other locations from the comfort of their own homes.

The High-Resolution Imaging Science Experiment (HiRISE) camera aboard NASA's Mars Reconnaissance Orbiter took this image of a crater cluster on Mars, the first ever to be discovered by artificial intelligence (AI). NASA said, "These craters were created by several pieces of a single meteor. The largest of the craters is about 13 feet (4 meters) wide. In total, the craters span about 100 feet (30 meters) of the red planet's surface. The craters were found in a region called Noctis Fossae, located at latitude -3.213, longitude 259.415."

According to the World Health Organization cancer is the second leading cause of death globally [1], and the most common cancer in the world is skin cancer [2]. To get a skin cancer diagnosis, a patient will usually mention their concern to a doctor. The doctor will then refer the patient to a dermatologist, who takes a closer look to determine whether the lesion is abnormal and in need of further inspection. If it's abnormal, the next step is usually a biopsy of the lesion for further testing in a laboratory. The test results determine the final diagnosis.

Twitter is making changes to its photo cropping function after an investigation into racial bias in the software, the company said on Thursday. The announcement comes after users on the platform repeatedly showed that the tool -- which uses machine learning to choose which part of an image to crop based on what it thinks is the most interesting -- cuts out Black people from photos and centers on white faces instead. Tony Arcieri, a cryptography engineer, posted a series of tweets in mid-September showing how the platform's algorithm routinely chose to highlight the face of Senate Majority Leader Mitch McConnell, who is white, instead of former President Barack Obama's in multiple photos of the two. The experiment prompted others to try similar experiments with the same result, and led to the company launching an investigation into its systems shortly after. The social media company implemented its machine-learning-powered image cropping system in 2018.

In an age of social distancing, machines are being used for everything from cleaning to taking temperatures

Driverless taxis are now available to the public in China's biggest city. A company backed by the online tech and retail giant Alibaba has started offering a free taxi service – using a fleet of autonomous driving cars – in a small area of Shanghai. Robin Brant took a ride.

Emerging technology is slowly finding a place in developing countries for its potential to plug gaps in ailing public service systems, such as healthcare. At the same time, cases of bias and discrimination that overlap with the complexity of algorithms have created a trust problem with technology. Promoting transparency in algorithmic decision-making through explainability can be pivotal in addressing the lack of trust with medical artificial intelligence (AI), but this comes with challenges for providers and regulators. In generating explainability, AI providers need to prioritise their accountability to patient safety given that the most accurate of algorithms are still opaque. There are also additional costs involved. Regulators looking to facilitate the entry of innovation while prioritising patient safety will need to look into ascertaining a reasonable level of explainability considering risk factors and the context of its use, and adaptive and experimental means of regulation. Artificial intelligence (AI) models across the globe have come under the scanner over ethical issues; for instance, Amazon's hiring algorithm reportedly discriminates against women,[1] and there is evidence of racial bias in the facial recognition software used by law enforcement in the United States (US).[2] While biased AI has various implications, concerns around the use of AI in ethically sensitive industries, such as healthcare, justifiably require closer examination. Medical AI models have become more commonplace in clinical and healthcare settings due to their higher accuracy and lower turnaround time and cost in comparison to non-AI techniques.

Inside an ordinary-looking home, a robot suspended from the ceiling slowly expands arms holding a sponge, before carefully wiping a kitchen surface clean. Nearby, another robot gently cleans a flat-screen television, causing it to wobble slightly. The cleaning robots live inside a mock home located at the Toyota Research Institute in Los Altos, California. The institute's researchers are testing a range of robot technologies designed to help finally realize the dream of a home robot. After looking at homes in Japan, which were often small and cluttered, the researchers realized they needed a creative solution.